Hot spot defect detecting method and hot spot defect detecting system

ABSTRACT

A hot spot defect detecting method and a hot spot defect detecting system are provided. In the method, hot spots are extracted from a design of a semiconductor product to define a hot spot map comprising hot spot groups, wherein local patterns in a same context of the design yielding a same image content are defined as a same hot spot group. During runtime, defect images obtained by an inspection tool performing hot scans on a wafer manufactured with the design are acquired and the hot spot map is aligned to each defect image to locate the hot spot groups. The hot spot defects in each defect image are detected by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 16/116,899, filed on Aug. 29, 2018, now allowed, which claims thepriority benefit of U.S. provisional application Ser. No. 62/656,997,filed on Apr. 13, 2018. The entirety of each of the above-mentionedpatent applications is hereby incorporated by reference herein and madea part of this specification.

BACKGROUND OF THE INVENTION

In the manufacturing processes of modem semiconductor devices, variousmaterials and machines are manipulated to create a final product.Manufacturers have dedicated to reduce particulate contamination duringprocessing so as to improve product yield. Due to the increasingcomplexity of semiconductor devices and the development of ultra-smalltransistors, the need for defect detection and control is furtheremphasized.

The inspection on the semi-manufactured product is frequently performedduring manufacturing by using optical inspection tool in order to timelyfind the defects. The sensitivity of existing optical inspection tool islimited by wafer noise. Since defect size continues to decrease alongwith advancement of process, the defect signals are becoming even weakerthan the wafer noise. As a result, those optical inspection tools beginto show more and more gaps in detecting various types of defects.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 illustrates a schematic block diagram of a hot spot defectdetecting system according to an embodiment of the disclosure.

FIG. 2 is a flowchart illustrating a method for detecting hot spotdefects according to an embodiment of the disclosure.

FIG. 3A to FIG. 3D are examples of extracting and grouping hot spotsaccording to one embodiment of the disclosure.

FIG. 4 is a schematic diagram illustrating a method for detecting hotspot defects according to an embodiment of the disclosure.

FIG. 5 is a schematic diagram illustrating a dynamic mapping mechanismaccording to an embodiment of the disclosure.

FIG. 6 is a flowchart illustrating a method for detecting hot spotdefects according to an embodiment of the disclosure.

FIG. 7 is a flowchart illustrating a method for selecting an optimaloptical mode according to an embodiment of the disclosure.

FIG. 8 is a flowchart illustrating a method for generating optimal imagefilters according to an embodiment of the disclosure.

FIG. 9 is an example of generating optimal image filters according to anembodiment of the disclosure.

DESCRIPTION OF THE EMBODIMENTS

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The apparatus may be otherwise oriented (rotated 90 degreesor at other orientations) and the spatially relative descriptors usedherein may likewise be interpreted accordingly.

Defects of interest (DOIs) are defects specific to an integrated circuitlayout of a semiconductor product that may occur at a specific area andform a local pattern during the manufacturing process of thesemiconductor product. In the present disclosure, the DOIs are detectedand identified in advance by using an optical inspection tool, andaccording to a design of a semiconductor product, local patterns of theintegrated circuit where defects of interest (DOIs) may actually occurare extracted as hot spots and grouped into multiple hot spot groups, inwhich the local patterns in a same design context that yield a sameimage content are defined as a same group while different local patternsor a same local pattern in different design contexts that may result indifferent image contents are split into separated groups. As for thehundreds or thousands of hot spot groups defined through aforesaidmethod, a dynamic mapping mechanism is adopted to map the hot spotgroups in each of the defect images acquired from the inspection tool toa limited number of threshold regions during runtime, and thus themethod may not only maximize the tool's sensitivity in detecting defectsbut also enable the inspection tool to handle virtually unlimited numberof hot spot groups.

FIG. 1 illustrates a schematic block diagram of a hot spot defectdetecting system according to an embodiment of the disclosure. Referringto FIG. 1, a hot spot defect detecting system 100 of the embodimentincludes a connecting device 110, a storage medium 120, and a processor130 coupled to the connecting device 110 and the storage medium 120.

In some embodiments, the hot spot defect detecting system 100 isexternally connected to at least one inspection tool (an opticalinspection tool 200 is taken as an example in the embodiment) andconfigured to acquire defect images imgs from the optical inspectiontool 200 by the connecting device 110, where the optical inspection tool200 is configured to perform hot scans on at least one wafer. The hotspot defect detecting system 100 is configured to analyse the acquireddefect images imgs to detect hot spot defects.

In some embodiments, the hot spot defect detecting system 100 may bedisposed or embedded in the optical inspection tool 200, which is notlimited herein. The hot spot defect detecting system 100 will bedescribed in detail in the following descriptions.

The connecting device 110 is configured to connect to the opticalinspection tool 200 to acquire defect images imgs from a plurality ofinspection images obtained by the optical inspection tool 200. Theconnecting device 110 is, for example, any wired or wireless interfacecompatible to the optical inspection tool 200 such as USB, firewire,thunderbolt, universal asynchronous receiver/transmitter (UART), serialperipheral interface bus (SPI), WiFi, or Bluetooth, which is not limitedherein.

The storage medium 120 is configured to store the defect images acquiredby the connecting device 110. The defect images from the opticalinspection tool 200 takes a considerable amount of memory storage, hencethe storage medium 120 is, for example, a mass storage device, aredundant array of independent disks (RAID), other similar storagedevice or a combination thereof having a high storage capacity, but thedisclosure is not limited thereto.

The processor 130 is configured to execute instructions for carrying outthe hot spot defect detecting method of the embodiments of thedisclosure. The processor 130 is, for example, a central processing unit(CPU), other programmable general-purpose or specific-purposemicroprocessors, a digital signal processor (DSP), a programmablecontroller, an application specific integrated circuit (ASIC), aprogrammable logic device (PLD), other similar devices, or a combinationthereof, but the disclosure is not limited thereto.

The hot spot defect detecting system 100 is adapted for carrying out ahot spot defect detecting method in accordance with some embodiments ofthe present disclosure. In detail, FIG. 2 is a flowchart illustrating amethod for detecting hot spot defects according to an embodiment of thedisclosure. Referring to FIG. 1 and FIG. 2, the method of the presentembodiment is adapted to the hot spot defect detecting system 100 ofFIG. 1, and detailed steps of the method of the present embodiment aredescribed below with reference to various components in the hot spotdefect detecting system 100 of FIG. 1.

In step S22, the processor 130 of the defect detecting system 100extracts a plurality of hot spots from a design of a semiconductorproduct to define a hot spot map comprising a plurality of hot spotgroups, in which a plurality of local patterns in a same context of thedesign yielding a same image content are defined as a same hot spotgroup. The hot spot map is, for example, stored in the storage medium120 for further use.

In some embodiments, various layout patterns of integrated circuitswhere defects may occur are previously identified and defined as thelocal patterns where the defects may occur by using the opticalinspection tool 200. Accordingly, the design of the semiconductorproduct is analysed such that the local patterns of integrated circuitsof the design matching with the previously defined layout patterns areextracted as the hot spots.

In some embodiments, for a given type of hot spot defect, there could bemultiple design contexts that can produce this hot spot defect. Thelocal pattern where the defect can actually occur could be quitedifferent among those design contexts. Different local patterns willresult in different image content (i.e. gray level and noise level atthe hot spot pixels) during runtime. Mixture of different image contentswill result in higher variation of noise level, making the defectsburied deeper in the noise cloud and therefore harder to be detected orsampled. Based on the above, only local patterns in the design thatyield the same image content are considered as belonging to the samegroup. If there are multiple design contexts that can produce a sametype of hot spot defects (i.e. with a same image content), they aresplit into separated groups according to the local pattern. In someembodiments, for each group, the location of each hot spot shall becentered on a location where the corresponding defect is most likely tooccur and the size of the hot spot shall be equal to or less than oneinspection pixel size.

For example, FIG. 3A to FIG. 3D are examples of extracting and groupinghot spots according to one embodiment of the disclosure. Referring theFIG. 3A and FIG. 3B, images 32 and 34 have a same design context andrespectively include hot spot images 32 a and 34 a that have a samelocal pattern and therefore the local pattern as shown in the hot spotimages 32 a and 34 a is extracted as one hot spot group. Referring theFIG. 3A and FIG. 3C, the image 36 includes a hot spot image 36 a thathas a local pattern different from the local pattern of the hot spotimage 32 a in the image 32 and therefore the hot spot image 36 a and thehot spot image 32 a are spitted into separated groups. Referring theFIG. 3A and FIG. 3D, the image 38 includes a hot spot image 38 a thathas a local pattern the same as the local pattern of the hot spot image32 a in the image 32 but has a different image context in an area otherthan the hot spot image 38 a (e.g. the upper conductive line in the hotspot image 38 is extended leftward while the upper conductive line inthe hot spot image 32 is extended upward).

Based on the above, the hot spots are grouped so that the noise level ofeach group is minimal during inspection and the sensitivity in detectingthe hot spot defects is maximized.

Back to the flow in FIG. 2, during runtime (i.e. the period that the hotspot defect detecting system 100 performs the defect detection on thewafer desired to be inspected), in step S24, the processor 130 acquiresa plurality of defect images obtained by the inspection tool performinghot scans on a wafer manufactured with the design and aligns the hotspot map to each of the defect images so as to locate the hot spotgroups in each defect image.

In some embodiments, the hot spot map including locations of hot spotgroups in the defect images is retrieved from the storage medium 120 bythe processor 130 and used to align with each of the defect images suchthat the hot spot groups in each defect image can be located.

In step S26, the processor 130 detects the hot spot defects in eachdefect image by dynamically mapping the hot spot groups located in eachdefect image to a plurality of threshold regions and respectivelyperforming automatic thresholding on pixel values of the hot spots ofeach hot spot group in the corresponding threshold region. In someembodiments, the threshold region refers to computing resource includingcomputing power and storage provided by the defect detecting system 100for performing automatic thresholding on one hot spot group, and anumber of threshold regions that can be supported by the defectdetecting system 100 depends on a computing capability of the processor130 and a storage capacity of the storage medium 120.

FIG. 4 is a schematic diagram illustrating a method for detecting hotspot defects according to an embodiment of the disclosure. In someembodiments, a set of test image 42 and a reference image 44 arecompared to detect the defects on the corresponding area of a wafer tobe inspected (not shown). The reference image 44 is, for example, animage obtained by the inspection tool performing hot scans on a previousdie in the wafer, in which the previous die is the die that the opticalinspection tool captures the image (i.e. the reference image 44) beforecapturing the image (i.e. the test image 42) of the die to be inspected.The comparison of the test image 42 and the reference image 44 fordetecting the hot spot defects could be implemented in various ways suchas statistical test. One exemplary embodiment is described below but itshould not be considered limiting the embodiment.

In some embodiments, a difference image DIFF of the test image 42 andthe reference image 44 which have been pre-processed through, forexample, histogram equalization is calculated, in which the pixel valueof each pixel in the difference image DIFF is a pixel value differencebetween the corresponding pixels of the test image 42 and the referenceimage 44. Most of the pixel values of the pixels in the difference imageDIFF should be around zero except for the pixels corresponding to thedefects. In some embodiments, a histogram 46 a of pixel values of thedifference image MIFF is calculated where the vertical axis of thehistogram 46 a represents the number of pixels, and the horizontal axisof the histogram 46 a represents the pixel values. By evaluating atleast one threshold T1 and T2 for differentiating the data points in thehistogram 46 a by using a statistical method (e.g. by using the lowerand upper quartiles of the ordered data points), an outlier O1 thatdeviates from other data points is determined, and the pixels having thepixel values corresponding to the outlier O1 of the histogram 46 a canbe determined as the defect. The aforementioned method is usuallyadopted by the inspection tool for detecting the defects on the testimage 42.

In some embodiments, the hot spot image 42 a in the test image 42 andthe hot spot image 44 a in the reference image 44 are respectivelylocated by aligning the hot spot map to the test image 42 and thereference image 44. A difference image diff of the hot spot image 42 aand the hot spot image 44 a is calculated, in which the pixel value ofeach pixel in the difference image diff is a pixel value differencebetween the corresponding pixels of the hot spot image 42 a and the hotspot image 44 a. Most of the pixel values of the pixels in thedifference image diff should be around zero except for the pixelscorresponding to the defects. In some embodiments, a histogram 48 a ofpixel values of the difference image diff is further calculated wherethe vertical axis of the histogram 48 a represents the number of pixels,and the horizontal axis of the histogram 48 a represents the pixelvalues. By evaluating at least one threshold T3 and T4 fordifferentiating the data points in the histogram 48 a by using astatistical method (e.g. by using the lower and upper quartiles of theordered data points), an outlier O2 that deviates from other data pointsis determined, and the pixels having the pixel values corresponding tothe outlier O2 of the histogram 48 a can be determined as the hot spotdefect. Compared to the detecting method using the images 42 and 44, thecalculation in the present method is specific to the hot spot images 42a and 44 a, so as to detect the hot spot defect on the test image 42.

In some embodiments, due to practical limitation of computing power, theinspection tool (analogy to the hot spot defect detecting system 100 ofthe embodiment) is designed with a limited number of threshold regions,which is, for example, 32 or 256. However, the grouping method asillustrated in step S22 of the present embodiment may potentially resultin hundreds or thousands of hot spot groups which are beyond thecapability of the inspection tool. Accordingly, in some embodiments, adynamic mapping mechanism that maps the hot spot groups to the limitednumber of threshold regions during runtime is provided.

FIG. 5 is a schematic diagram illustrating a dynamic mapping mechanismaccording to an embodiment of the disclosure. In some embodiments,although hundreds or thousands of hot spot groups are defined, those hotspot groups usually do not simultaneously occur in one defect image.Instead, the number of hot spot groups that actually occur in eachdefect image is limited, and therefore the hot spot groups in eachdefect image may be dynamically mapped to the threshold regions 50 forsubsequent automatic thresholding.

For example, in the defect image 52, hot spot images respectivelycorresponding to hot spot groups numbered 95, 31, 50 and 999 are locatedby aligning the hot spot map to the defect image 52 and the hot spotgroups 95, 31, 50 and 999 are dynamically mapped to the thresholdregions 1 to 4. In each of the threshold regions 1 to 4, at least adetection threshold for the threshold region is determined based onnoise levels of the pixels of the hot spots of each hot spot group, andthe pixels having the pixel values deviating from the detectionthreshold are determined as the hot spot defect.

Similarly, in the defect image 54, hot spot images respectivelycorresponding to hot spot groups numbered 96, 37 and 4 are located byaligning the hot spot map to the defect image 54 and the hot spot groups96, 37 and 4 are dynamically mapped to the threshold regions 1 to 3. Ineach of the threshold regions 1 to 3, at least a detection threshold forthe threshold region is determined based on noise levels of the pixelsof the hot spots of each hot spot group, and the pixels having the pixelvalues deviating from the detection threshold are determined as the hotspot defect.

The defect images subsequently acquired are sequentially mapped to thethreshold regions 50 for automatic thresholding until all the defectimages are processed. For the threshold regions where the hot spotdefects are detected, a region number of the threshold region is mappedback to the hot spot group so as to confirm the types of hot spot groupsoccurring in the defect images.

Based on the above, through the dynamic mapping mechanism that maps thehot spot groups to the limited number of threshold regions duringruntime, the method of the present embodiment may enable the inspectiontool to handle virtually unlimited number of hot spot groups.

In some embodiments, in addition to the method for extracting andgrouping the hot spots and dynamic mapping the hot spot groups, amachine learning technique is further adopted to find the best operationmode of the inspection tool and the optimal image filters for detectingthe hot spot defect.

In detail, FIG. 6 is a flowchart illustrating a method for detecting hotspot defects according to an embodiment of the disclosure. Referring toFIG. 1 and FIG. 6, the method of the present embodiment is adapted tothe hot spot defect detecting system 100 of FIG. 1, and detailed stepsof the method of the present embodiment are described below withreference of various components of the hot spot defect detecting system100 of FIG. 1.

In step S62, the processor 130 of the defect detecting system 100acquires a plurality of defect images of a plurality of optical modesobtained by an inspection tool performing hot scans on a wafermanufactured with a design of a semiconductor product under variousoptical modes and selects an optimal optical mode for detecting the hotspot defects from among the optical modes based on a separability ofdefects to nuisances in the defect images for each optical mode.

In some embodiments, in various optical modes, different parameters suchas intensity and wavelength of the incident light, lens aperture, orexposure time are applied for operating the optical inspection tool soas to find the best mode for detecting the hot spot defects.

FIG. 7 is a flowchart illustrating a method for selecting an optimaloptical mode according to an embodiment of the disclosure. Referring toFIG. 7, the method of the present embodiment illustrates the detailedsteps of the step S62 in FIG. 6.

In step S621, the processor 130 acquires a plurality of defect images ofa plurality of optical modes from the inspection tool. The defect imagesacquired by the processor 130 from the inspection tool may includedefects and/or nuisances.

In step S622, the processor 130 aligns the hot spot map to the defectimage of each optical mode to locate the hot spot defects.

In step S623, the processor 130 computes a signal level and a noiselevel of each of the hot spot defects in the defect image of eachoptical mode.

In step S624, the processor 130 computes the separability of defects tonuisances for each optical mode by summarizing ratios of the signallevel to the noise level of the hot spot defects.

In step S625, the processor 130 ranks the optical modes according to thecomputed separabilities so as to select the optimal optical mode.

Back to the flow in FIG. 6, in step S64, the processor 130 trains amachine learning model for classifying the defects from the nuisanceswith the defect images of the selected optimal optical mode so as toevaluate optimal filters for detecting the hot spot defects for theoptimal optical mode.

In some embodiments, a convolution neural network (CNN) model is createdand trained with defect images and nuisance images so as to find optimalfilters for classifying the defects and the nuisances.

FIG. 8 is a flowchart illustrating a method for generating optimal imagefilters according to an embodiment of the disclosure. Referring to FIG.8, the method of the present embodiment illustrates the detailed stepsof the step S64 in FIG. 6.

In step S641, the processor 130 creates a machine learning model withconvolution filters for processing the defect images.

In step S642, the processor 130 feeds a plurality of defect imagesincluding defects and/or nuisances of the selected optical mode to themachine learning model to train the machine learning model forclassifying the defects from the nuisances in the defect images.

In step S643, the processor 130 adopts the convolution filters of thetrained machine learning model as optimal filters for detecting the hotspot defects.

For example, FIG. 9 is an example of generating optimal image filtersaccording to an embodiment of the disclosure. Referring to FIG. 9, aplurality of defect images (including images 92 a and 92 b) and aplurality of nuisance images (including images 94 a and 94 b) are fedinto a convolution neural network 96 which includes multiple inputlayers, multiple convolution layers (two convolution layers are taken asan example in the present embodiment), and an output layer to train theconvolution neural network 96 to classify defects from nuisances. Theconvolution filters created from the convolution layers of the trainedconvolution neural network 96 are adopted as the optimal filters 98 fordetecting the hot spot defects.

As a result, the image filters optimized to separate hot spot defectsfrom nuisances are generated, and the generated image filters areapplied to the pixel values of the hot spots of each hot spot group inthe corresponding threshold region so as to filter out nuisance imagesfrom the defect images.

Back to the flow in FIG. 6, in step S66, the processor 130 acquires aplurality of defect images obtained by the inspection tool performinghot scans on the wafer under the optimal optical mode in runtime andaligns a hot spot map comprising a plurality of groups of hot spotsextracted from the design to each of the defect images to locate the hotspot groups in each defect image.

In step S68, the processor 130 detects the hot spot defects in eachdefect image by dynamically mapping the hot spot groups located in eachdefect image to a plurality of threshold regions, applying the optimalfilters to the pixel values of the hot spots of each hot spot group inthe corresponding threshold region, and respectively performingautomatic thresholding on pixel values of the hot spots of each hot spotgroup in the corresponding threshold region.

Through applying the optimal filters to the pixel values of the hotspots before performing the automatic thresholding, the nuisances can befound and filtered from the defect images such that an accuracy fordetecting the hot spot defects can be enhanced.

Through the method, the present disclosure provides one or more of thefollowing advantages: (1) defining most effective hot spots; (2)learning optimal optical mode and filtering parameters; (3) efficientlyprocessing the hot spot scan data on inspection tools; and (4) enablinginspection tools to gain sensitivity on smallest defects of interest(DOIs) beyond current capability.

According to some embodiments, a hot spot defect detecting methodadapted to an electronic apparatus is provided. In the method, aplurality of hot spots are extracted from a design of a semiconductorproduct to define a hot spot map comprising a plurality of hot spotgroups, wherein a plurality of local patterns in a same context of thedesign yielding a same image content are defined as a same hot spotgroup. A plurality of defect images obtained by an inspection toolperforming hot scans on a wafer manufactured with the design areacquired during runtime and the hot spot map is aligned to each of thedefect images to locate the hot spot groups in each defect image. Thehot spot defects in each defect image are detected by dynamicallymapping the hot spot groups located in each defect image to a pluralityof threshold regions and respectively performing automatic thresholdingon pixel values of the hot spots of each hot spot group in thecorresponding threshold region.

According to some embodiments, a system for detecting hot spot defectsincludes a connecting device configured to connect an inspection tool, astorage medium configured to store the images acquired by the connectingdevice, and a processor coupled to the connecting device and the storagemedium. The processor is configured to execute instructions to performsteps of extracting a plurality of hot spots from a design of asemiconductor product to define a hot spot map comprising a plurality ofhot spot groups, wherein a plurality of local patterns in a same contextof the design yielding a same image content are defined as a same hotspot group; acquiring a plurality of defect images obtained by aninspection tool performing hot scans on a wafer manufactured with thedesign during runtime and aligning the hot spot map to each of thedefect images to locate the hot spot groups in each defect image; anddetecting the hot spot defects in each defect image by dynamicallymapping the hot spot groups located in each defect image to a pluralityof threshold regions and respectively performing automatic thresholdingon pixel values of the hot spots of each hot spot group in thecorresponding threshold region.

According to some embodiments, a hot spot defect detecting methodadapted to an electronic apparatus is provided. In the method, aplurality of defect images of a plurality of optical modes obtained byan inspection tool performing hot scans on a wafer manufactured with adesign of a semiconductor product under the optical modes are acquiredand an optimal optical mode for detecting the hot spot defects isselected from among the optical modes based on a separability of defectsto nuisances in the defect images for each optical mode. A machinelearning model for classifying the defects from the nuisances is trainedwith the defect images of the selected optimal optical mode to evaluateoptimal filters for detecting the hot spot defects for the optimaloptical mode. A plurality of defect images obtained by the inspectiontool performing hot scans on the wafer under the optimal optical modeare acquired in runtime and a hot spot map comprising a plurality ofgroups of hot spots extracted from the design is aligned to each of thedefect images to locate the hot spot groups in each defect image. Thehot spot defects in each defect image are detected by dynamicallymapping the hot spot groups located in each defect image to a pluralityof threshold regions, applying the optimal filters to the pixel valuesof the hot spots of each hot spot group in the corresponding thresholdregion, and respectively performing automatic thresholding on pixelvalues of the hot spots of each hot spot group in the correspondingthreshold region.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method for detecting hot spot defects, adaptedto an electronic apparatus, the method comprising: acquiring a pluralityof defect images obtained by an inspection tool performing hot scans ona wafer manufactured with the design during runtime and aligning a hotspot map comprising a plurality of hot spot groups extracted from adesign of the wafer to each of the defect images to locate the hot spotgroups in each defect image; for each of the defect images, dynamicallymapping each of the hot spot groups located in the respective defectimage to one of a plurality of threshold regions; and for each of thethreshold regions, determining at least a detection threshold based onnoise levels of the pixels of each hot spot group in the respectivethreshold region, and determining the pixels having the pixel valuesdeviating from the detection threshold as the hot spot defect.
 2. Themethod according to claim 1, further comprising: acquiring a pluralityof defect images obtained by the inspection tool performing hot scans,under a plurality of optical modes, on the wafer manufactured with thedesign; and selecting an optimal optical mode for detecting the hot spotdefects from among the plurality of optical modes based on aseparability of defects to nuisances in the defect images obtained undereach optical mode, wherein the defect images obtained by the inspectiontool performing hot scans on the wafer manufactured with the designunder the selected optimal optical mode are acquired for detecting thehot spot defects.
 3. The method according to claim 2, wherein the stepof selecting the optimal optical mode comprises: aligning the hot spotmap to the defect image of each optical mode to locate the hot spotdefects; computing a signal level and a noise level of each of the hotspot defects in the defect image of each optical mode; computing theseparability for each optical mode by summarizing ratios of the signallevel to the noise level of the hot spot defects; and ranking theoptical modes according to the computed separabilities to select theoptimal optical mode.
 4. The method according to claim 2, furthercomprising: training a machine learning model for classifying thedefects from the nuisances with the defect images of the selectedoptical mode to evaluate optimal filters for detecting the hot spotdefects.
 5. The method according to claim 4, wherein the step oftraining the machine learning model for classifying the defects from thenuisances with the defect images of the selected optical mode toevaluate the optimal filters for detecting the hot spot defectscomprises: creating the machine learning model with convolution filtersfor processing the defect images; feeding a plurality of defect imagesof the selected optical mode to the machine learning model to train themachine learning model for classifying the defects from the nuisances inthe defect images; and adopting the convolution filters of the trainedmachine learning model as the optimal filters for detecting the hot spotdefects, wherein the optimal filters are applied to the pixel values ofthe hot spots of each hot spot group in the corresponding thresholdregion before performing the automatic thresholding.
 6. The methodaccording to claim 1, wherein a plurality of local patterns extractedfrom a design of a semiconductor product are defined as a plurality ofhot spots and a plurality of local patterns in a same context of thedesign yielding a same image content are defined as the hot spots of asame hot spot group.
 7. The method according to claim 1, wherein defectsdetected in each of the threshold region are mapped to the correspondinghot spot groups as the hot spot defects in the defect image.
 8. A systemfor detecting hot spot defects, comprising: a connecting device,configured to connect an inspection tool; a storage medium, configuredto store images acquired by the connecting device; a processor, coupledto the connecting device and the storage medium, and configured toexecute instructions to perform steps of: acquiring a plurality ofdefect images obtained by an inspection tool performing hot scans on awafer manufactured with the design during runtime and aligning a hotspot map comprising a plurality of hot spot groups extracted from adesign of the wafer to each of the defect images to locate the hot spotgroups in each defect image; for each of the defect images, dynamicallymapping each of the hot spot groups located in the respective defectimage to one of a plurality of threshold regions; and for each of thethreshold regions, determining at least a detection threshold based onnoise levels of the pixels of each hot spot group in the respectivethreshold region, and determining the pixels having the pixel valuesdeviating from the detection threshold as the hot spot defect.
 9. Thesystem according to claim 8, wherein the processor is further configuredto execute instructions to perform steps of: acquiring a plurality ofdefect images obtained by the inspection tool performing hot scans,under a plurality of optical modes, on the wafer manufactured with thedesign; and selecting an optimal optical mode for detecting the hot spotdefects from among the plurality of optical modes based on aseparability of defects to nuisances in the defect images obtained undereach optical mode, wherein the defect images obtained by the inspectiontool performing hot scans on the wafer manufactured with the designunder the selected optimal optical mode are acquired for detecting thehot spot defects.
 10. The system according to in claim 9, wherein theprocessor is further configured to execute instructions to perform stepsof: aligning the hot spot map to the defect image of each optical modeto locate the hot spot defects; computing a signal level and a noiselevel of each of the hot spot defects in the defect image of eachoptical mode; computing the separability for each optical mode bysumming ratios of the signal level to the noise level of the hot spotdefects; and ranking the optical modes according to the computedseparabilities to select the optimal optical mode.
 11. The systemaccording to claim 9, wherein the processor is further configured toexecute instructions to perform a step of: training a machine learningmodel for classifying the defects from the nuisances with the defectimages of the selected optical mode to evaluate optimal filters fordetecting the hot spot defects.
 12. The system according to claim 11,wherein the processor is further configured to execute instructions toperform steps of: creating the machine learning model with convolutionfilters for processing the defect images; feeding a plurality of defectimages of the selected optical mode to the machine learning mode totrain the machine learning model for classifying the defects from thenuisances in the defect images; and adopting the convolution filters ofthe trained machine learning model as optimal filters for detecting thehot spot defects, wherein the optimal filters are applied to the pixelvalues of the hot spots of each hot spot group in the correspondingthreshold region before performing the automatic thresholding.
 13. Thesystem according to claim 8, wherein a plurality of local patternsextracted from a design of a semiconductor product are defined as aplurality of hot spots and a plurality of local patterns in a samecontext of the design yielding a same image content are defined as thehot spots of a same hot spot group.
 14. The system according to claim 8,wherein the processor maps defects detected in each of the thresholdregion to the corresponding hot spot groups as the hot spot defects inthe defect image.
 15. A method for detecting hot spot defects, adaptedto an electronic apparatus, the method comprising: acquiring a pluralityof defect images obtained by an inspection tool performing hot scans ona wafer manufactured with a design, and training a machine learningmodel for classifying the defects from the nuisances with the defectimages to evaluate optimal filters for detecting the hot spot defects;and acquiring a plurality of defect images obtained by the inspectiontool performing hot scans on the wafer in runtime and aligning a hotspot map comprising a plurality of hot spot groups extracted from thedesign to each of the defect images to locate the hot spot groups ineach defect image; for each of the defect images, dynamically mappingeach of the hot spot groups located in the respective defect image toone of a plurality of threshold regions; and for each of the thresholdregions, applying the optimal filters to the pixel values of the hotspots of each hot spot group, and determining at least a detectionthreshold based on noise levels of the pixels of each hot spot group inthe respective threshold region, and determining the pixels having thepixel values deviating from the detection threshold as the hot spotdefect.
 16. The method according to claim 15, wherein the step ofacquiring the plurality of defect images and training the machinelearning model comprises: acquiring a plurality of defect imagesobtained by the inspection tool performing hot scans, under a pluralityof optical modes, on a wafer manufactured with a design of asemiconductor product and selecting an optimal optical mode fordetecting the hot spot defects from among the plurality of optical modesbased on a separability of defects to nuisances in the defect imagesobtained under each optical mode; and training the machine learningmodel for classifying the defects from the nuisances with the defectimages obtained under the selected optimal optical mode to evaluateoptimal filters for detecting the hot spot defects for the optimaloptical mode.
 17. The method according to claim 16, wherein the step ofselecting the optimal optical mode comprises: aligning the hot spot mapto the defect image of each optical mode to locate the hot spot defects;computing a signal level and a noise level of each of the hot spotdefects in the defect image of each optical mode; computing theseparability for each optical mode by summing ratios of the signal levelto the noise level of the hot spot defects; and ranking the opticalmodes according to the computed separabilities to select the optimaloptical mode.
 18. The method according to claim 16, wherein the step oftraining the machine learning model for classifying the defects from thenuisances with the defect images obtained under the selected opticalmode to evaluate the optimal filters for detecting the hot spot defectsfor the optimal optical mode comprises: creating the machine learningmodel with convolution filters for processing the defect images; feedinga plurality of defect images of the selected optical mode to the machinelearning mode to train the machine learning model for classifying thedefects from the nuisances in the defect images; and adopting theconvolution filters of the trained machine learning model as optimalfilters for detecting the hot spot defects, wherein the optimal filtersare applied to the pixel values of the hot spots of each hot spot groupin the corresponding threshold region before performing the automaticthresholding.
 19. The method according to claim 15, wherein defectsdetected in each of the threshold region are mapped to the correspondinghot spot groups as the hot spot defects in the defect image.
 20. Themethod according to claim 15, wherein the machine learning modelcomprises a convolution neural network (CNN) model.